Computer Science ›› 2019, Vol. 46 ›› Issue (8): 327-331.doi: 10.11896/j.issn.1002-137X.2019.08.054

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Low Light Images Enhancement Based on Retinex Adaptive Reflectance Estimation and LIPS Post-processing

PAN Wei-qiong, TU Juan-juan, GAN Zong-liang, LIU Feng   

  1. (Jiangsu Province Key Lab on Image Processing and Image Communication,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
  • Received:2018-07-31 Online:2019-08-15 Published:2019-08-15

Abstract: Due to the influence of strong light,the images acquired at night have high contrast,the same situation also appears in backlit images collected in the daytime.Contrast enhancement method is usually applied to the images for obtaining images with favorable contrast.Whereas,over-enhancement commonly occurs in bright regions.Accordingly,in order to solve the problem of over-enhancement for high contrast images,a Retinex based low light image enhancement algorithm through adaptive reflection component estimation and logarithmic image processing subtraction post-proces-sing was proposed.The algorithm mainly includes into two parts:reflection component estimation and logarithmic image processing subtraction (LIPS) enhancement.First,adaptive parameter bilateral filters are used to get more accu-rate illumination layer data,instead of Gaussian filter.Moreover,the weighting estimation method is used to calculate the adaptive parameter to adjust the removal of the illumination and obtain the reflectance by just-noticeable-distortion (JND)factor.In this way,it can effectively prevent the over-enhancement in high-brightness regions.Then,the LIPS method based on maximum standard deviation of the histogram is applied to enhance reflectance component part,where the interval of the parameter is according to the cumulative distribution function (CDF).Experimental results demonstrate that the proposed method outperforms other competitive methods in terms of subjective and objective assessment

Key words: Reflectance estimation, Logarithmic image processing subtraction, Just-noticeable-distortion, Maximum standard deviation

CLC Number: 

  • TP391.41
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